Single-Turn Debate Does Not Help Humans Answer Hard Reading-Comprehension Questions

By Alicia Parrish, Harsh Trivedi, Ethan Perez, Angelica Chen, Nikita Nangia, Jason Phang, Sam Bowman, Samuel R. Bowman. 2022

21 citations recorded by the source corpus.

Current QA systems can generate reasonable-sounding yet false answers without explanation or evidence for the generated answer, which is especially problematic when humans cannot readily check the model’s answers. This presents a challenge for building trust in machine learning systems. We take inspiration from real-world situations where difficult questions are answered by considering opposing sides (see Irving et al., 2018). For multiple-choice QA examples, we build a dataset of single arguments for both a correct and incorrect answer option in a debate-style set-up as an initial step in training models to produce explanations for two candidate answers. We use long contexts—humans familiar with the context write convincing explanations for pre-selected correct and incorrect answers, and we test if those explanations allow humans who have not read the full context to more accurately determine the correct answer. We do not find that explanations in our set-up improve human accuracy, but a baseline condition shows that providing human-selected text snippets does improve accuracy. We use these findings to suggest ways of improving the debate set up for future data collection efforts.

Subfields: Natural Language Processing

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Citations in the corpus

Improving alignment of dialogue agents via targeted human judgements (2022, background): “Initial evidence from one-step debate is mixed: Saunders et al. (2022) find that model-generated critiques help humans notice flaws in summaries, but in Parrish et al. (2022) accuracy did not improve when humans were shown explanations.”
The alignment problem from a deep learning perspective (2022, methodology): “To address this, RLHF has been used to train policies to assist human supervisors, e.g. by critiquing the main policy’s outputs in natural language (albeit with mixed results thus far) [Saunders et al., 2022, Parrish et al., 2022b,a, Bowman et al., 2022, Bai et al., 2022b].”
Two-Turn Debate Doesn't Help Humans Answer Hard Reading Comprehension Questions (2022, methodology): “The writers’ task is to construct a counter-argument arguing against the existing argument from Parrish et al. (2022).”